Computer-based systems having computing devices programmed to execute fraud detection routines based on feature sets associated with input from physical cards and methods of use thereof

ABSTRACT

Systems and methods for performing fraud detection at POA devices based on analysis of feature sets are disclosed. In one embodiment, an exemplary method may comprise: obtaining, by a POS device, upon initiation of a transaction involving a card or a card and mobile device associated with an individual initiating the transaction, one or more sensory inputs and an identifier; mapping, by the POS device, the one or more sensory inputs to a first cluster position of a plurality of clusters; determining whether the cluster position of the cluster mapped for the transaction corresponds to a second cluster position of the at least one expected cluster associated with the known owner of the card and/or mobile device; and initiating, by the POS device, at least one second factor authentication process to establish that the individual is the known owner of the card and/or mobile device being used in the transaction.

CROSS-REFERENCE TO RELATED APPLICATION(S) INFORMATION

This is a continuation of U.S. application Ser. No. 16/695,993, filedNov. 26, 2019, now U.S. Pat. No. 10,748,155, which are incorporatedherein by reference in entirety.

COPYRIGHT NOTICE

A portion of the disclosure of this patent document contains materialthat is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction by anyone of the patent documentor the patent disclosure, as it appears in the Patent and TrademarkOffice patent files or records, but otherwise reserves all copyrightrights whatsoever. The following notice applies to the software and dataas described below and in drawings that form a part of this document:Copyright, Capital One Services, LLC., All Rights Reserved.

FIELD OF TECHNOLOGY

The present disclosure generally relates to an improved computer-basedplatform or system, improved computing components and devices, andimproved computing methods configured for one or more noveltechnological applications involving performing fraud detection forcard-based transactions at POS devices.

BACKGROUND OF TECHNOLOGY

A computer network platform or system may include a group of computers(e.g., clients, servers, computing clusters, cloud resources, etc.) andother computing hardware devices that are linked and communicate viasoftware architecture, communication applications, or softwareapplications associated with electronic transactions, data processing,or account management.

SUMMARY OF DESCRIBED SUBJECT MATTER

In some embodiments, the present disclosure provides various exemplarytechnically improved computer-implemented methods for performing frauddetection for card-based transactions at a POS (point-of-service, orpoint-of-sale) device, including a method having steps such as:

obtaining, by a point-of-service (POS) device, upon initiation of atransaction involving (1) a card or (2) the card and a mobile deviceassociated with an individual transacting with the POS device:

-   -   (i) one or more sensory inputs associated with a use of the one        or both of the card and the mobile device for the transaction;        and    -   (ii) an identifier (1) associated with a known owner of the one        or both of the card and the mobile device and (2) used to        determine whether the one or more sensory inputs are consistent        with prior transaction behavior of the owner;

mapping, by the POS device, the one or more sensory inputs to a firstcluster position of at least one particular cluster of a plurality ofclusters,

-   -   wherein the plurality of clusters defines sets of hashed,        learned features regarding prior known interactions of owners of        cards with POS devices, the prior known interactions being        mapped by machine learning into the plurality of clusters such        that prior sensory input of each owner has been mapped to at        least one expected cluster;

determining, by the POS device, when the cluster position of the atleast one particular cluster mapped for the transaction matches a secondcluster position of the at least one expected cluster associated withthe known owner of the one or both of the card and mobile device; and

initiating, by the POS device, at least one second factor authenticationprocess to establish that the individual transacting with the POS deviceis the known owner of the one or both of the card and the mobile devicebeing used in the transaction when the first cluster position of the atleast one particular cluster does not correspond to the second clusterposition of the at least one expected cluster.

In some embodiments, the present disclosure also provides exemplarytechnically improved computer-based systems and computer-readable media,including media implemented with or involving one or more softwareapplications, whether resident on computer devices or platforms,provided for download via a server or executed in connection with atleast one network such as via a web browser application, that include orinvolves features, functionality, computing components or stepsconsistent with those set forth herein.

BRIEF DESCRIPTION OF THE DRAWINGS

Various embodiments of the present disclosure can be further explainedwith reference to the attached drawings, wherein like structures arereferred to by like numerals throughout the several views. The drawingsshown are not necessarily to scale, with emphasis instead generallybeing placed upon illustrating the principles of the present disclosure.Therefore, specific structural and functional details disclosed hereinare not to be interpreted as limiting, but merely as a representativebasis for teaching one skilled in the art to variously employ one ormore illustrative embodiments.

FIG. 1 is a block diagram of an exemplary system or platform involvingfeatures of performing fraud detection for card-based transactions at aPOS device, consistent with exemplary aspects of certain embodiments ofthe present disclosure.

FIG. 2 is a block diagram of an exemplary transaction card, consistentwith exemplary aspects of certain embodiments of the present disclosure.

FIG. 3 is a flowchart illustrating one exemplary process related toperforming fraud detection for card-based transactions at a POS device,consistent with exemplary aspects of certain embodiments of the presentdisclosure.

FIG. 4 is a block diagram depicting an exemplary computer-based systemor platform, in accordance with certain embodiments of the presentdisclosure.

FIG. 5 is a block diagram depicting another exemplary computer-basedsystem or platform, in accordance with certain embodiments of thepresent disclosure.

FIGS. 6 and 7 are diagrams illustrating two exemplary implementations ofcloud computing architecture or aspects with respect to which thedisclosed technology may be specifically configured to operate, inaccordance with certain embodiments of the present disclosure.

DETAILED DESCRIPTION

Various detailed embodiments of the present disclosure, taken inconjunction with the accompanying figures, are disclosed herein;however, it is to be understood that the disclosed embodiments aremerely illustrative. In addition, each of the examples given inconnection with the various embodiments of the present disclosure isintended to be illustrative, and not restrictive.

Throughout the specification, the following terms take the meaningsexplicitly associated herein, unless the context clearly dictatesotherwise. The phrases “in one embodiment” and “in some embodiments” asused herein do not necessarily refer to the same embodiment(s), thoughit may. Furthermore, the phrases “in another embodiment” and “in someother embodiments” as used herein do not necessarily refer to adifferent embodiment, although it may. Thus, as described below, variousembodiments may be readily combined, without departing from the scope orspirit of the present disclosure.

As explained in more detail, below, systems and methods for performingfraud detection at POA devices based on analysis of feature sets aredisclosed. In one embodiment, an exemplary method may comprise:obtaining, by a POS device, upon initiation of a transaction involving acard or a card and mobile device associated with an individualinitiating the transaction, one or more sensory inputs and anidentifier; mapping, by the POS device, the one or more sensory inputsto a first cluster position of a plurality of clusters; determiningwhether the cluster position of the cluster mapped for the transactioncorresponds to a second cluster position of the at least one expectedcluster associated with the known owner of the card and/or mobiledevice; and initiating, by the POS device, at least one second factorauthentication process to establish that the individual is the knownowner of the card and/or mobile device being used in the transaction. Insome embodiments, fraud detection is performed by merchants at the POSdevice based on a fuzzy-hash of features provided by a transaction card.

In some embodiments, the innovations herein may be implemented inconnection with a financial service entity that provides, maintains,manages, or otherwise offers financial services. Such financial serviceentity may be a bank, credit card issuer, or any other type of financialservice entity that generates, provides, manages, and/or maintainsfinancial service accounts that entail providing a transaction card forone or more customers, the transaction card being used at a POS devicein regard to such financial services. Financial service accounts mayinclude, for example, credit card accounts, bank accounts such aschecking and/or savings accounts, reward or loyalty program accounts,debit account, and/or any other type of financial service account knownto those skilled in the art.

FIG. 1 depicts an exemplary system 100 for improved fraud detectionassociated with usage of a transaction card by a user, in accordancewith one or more embodiments of the present disclosure. System 100 mayinclude a server 101, a mobile device 160, a transaction card 110 withcircuitry 113 disposed therein, and a point-of-service or point-of-sale(POS) device 195, which may all communicate 103 over a communicationnetwork 105. As used herein, “POS device” is defined to include bothconventional POS devices known in the art, re-programmed and/or retooled(software and/or hardware) conventional POS devices, as well as othercomputing devices, such as smartphones, tablets, computers, etc., havingphysical component to obtain input from cards by way of contact and/orcontactless interaction. When a user attempts a transaction with atransaction card, the business or merchant associated with the POSdevice 195 and typically a financial institution, such as a credit cardcompany that has issued the card to the user, may wish to assess whetherthe user using the credit card is the authorized user in order toapprove the transaction. Some embodiments herein may also leverage thefact that the user of the transaction card may most likely carry or isnear to the user's mobile device, such as a cellphone, tablet orsmartphone, for example, and may use functionality associated with theuser's mobile device and the transaction card, including interactionbetween the two, as a part of various fraud detection and/orauthentication processes for approving a transaction and/or authorizingthe user to use the transaction card to purchase goods or services.

In some embodiments, server 101 may be associated with one or moreentities that are stakeholders to the attempted transaction, such as thebusiness or merchant, one or more financial services providers, such asan issuer of a credit card, debit card, or other transaction cardassociated with the attempted transaction.

In some embodiments, transaction card 110 may include various circuitry113 including circuitry capable of communicating 132 variouscard-related information from the transaction card 113 to the POS device195. Such card-related information may include one or both of: (i) oneor more sensory inputs associated with a use of the one or both of thecard and the mobile device for the transaction, and/or (ii) anidentifier (1) associated with a known owner of the one or both of thecard and the mobile device and (2) used to determine whether the one ormore sensory inputs are consistent with prior transaction behavior ofthe owner.

In the embodiment shown in FIG. 1, an illustrative POS device 195 maycomprise: one or more processing components and/or computer readablemedia 170, memory 180, communication circuitry and/or interfaces 185, atleast one card reading component 190, and at least one cluster of dataregarding known card-usage behavior of a plurality of card holders. Thecard reading component(s) 190 may be configured to read information froma transaction card 110, for example, the at least one card readingcomponent may comprise one or more of a magnetic stripe reader, a chipreader, and/or a first near field communication (NFC) component.Communication circuitry and/or interfaces 185 may comprise at least onemobile device transceiver component configured to communicate, duringexecution of a purchase transaction, with a mobile device 160 presentedfor payment, the mobile device transceiver component comprising a secondNFC component.

With regard to the disclosed innovations, the processing componentsand/or computer readable media 170 may be configured to executeinstructions associated with performing methods such as that describedbelow in more detail in connection with FIG. 3. By way of example, invarious embodiments, the one or more processing components and/orcomputer readable media 170 may be configured for: obtaining, from oneor both of the transaction card 110 and/or the mobile device 160, uponinitiation of the transaction: one or more sensory inputs, and/or anidentifier; mapping the one or more sensory inputs to a first clusterposition of at least one particular cluster of a plurality of clusters195; determining whether the first cluster position of the at least oneparticular cluster mapped for the transaction corresponds to a secondcluster position of the at least one expected cluster associated withthe known owner of the one or both of the card 110 and mobile device160; and initiating at least one second factor authentication process toestablish that the individual transacting with the POS device is theknown owner of the one or both of the card 110 and the mobile device 160being used in the transaction.

According to various embodiments, the sensory inputs may be configuredto be associated with a user of the card 110 and/or the mobile device160 involved in the attempted transaction. In some embodiments, such anidentifier may be configured to be: (1) associated with a known owner ofthe one or both of the card and the mobile device, and/or (2) used todetermine whether the one or more sensory inputs are consistent withprior transaction behavior of the owner. In some embodiments, thesensory inputs may be combined with various information regarding thetransaction card and/or the user or owner, to create a set of features(or ‘feature set’) regarding the attempted transaction.

Historical collections of such feature set data, i.e., for each owner ofa transaction card and/or mobile device, are then assembled andprocessed into a set of clusters comprised of a plurality of clustersthat are each associated with sets of the features that define typicalcard transactions regarding which each user in question commonlyperforms. A set of recent historical feature information for any oneuser may define a first cluster from among the plurality of clusters, towhich that user's normal transaction features will map.

In some embodiments, the clusters are created and redistributed by theserver 101, for example, by a financial service provider running theserver 101. Once created and/or revised, the plurality of clusters maybe provided (e.g., by download or by chip or card) to POS devices, toprovide the merchants associated with the POS devices a set of clusters195 into which all customers of the financial service providers aremapped. This allows the merchants and POS devices to determine, bycomparison of data received from the transaction card against theclusters 195 locally-stored in the POS device, whether the currenttransaction is more or less likely to be a fraudulent transaction.

Further, according to some embodiments, the clusters 192 may beconfigured to define sets of features regarding prior known interactionsof owners of cards with POS devices 195. According to embodiments,herein, a variety of mathematical techniques may be utilized to createthe plurality of clusters from card owners' historical card-usageinformation. For example, the plurality of clusters may be created byone or more machine learning algorithms, by hashing the feature data, byusing a point-distance function of the historical feature data, by useof vector mathematics to map the historical data into the clusters,and/or by other known clustering techniques. According to exemplaryembodiments discussed herein, the prior transaction data may beconfigured into clusters that define sets of hashed, and/or learnedfeatures regarding prior known interactions of owners of cards with POSdevices. Further, the prior known interactions may be configured to bemapped by machine learning techniques into the plurality of clusters.Here, for example, the prior known interactions may be configured to bemapped by machine learning techniques into the plurality of clusterssuch that learned features including prior sensory inputs for each cardowner have been mapped to at least one expected cluster.

In some instances, such as when the first cluster position of the atleast one particular cluster does not correspond to the second clusterposition of the at least one expected cluster, the POS device (e.g., viathe processing components and/or computer readable media) may beconfigured to provide risk assessment and/or set a risk score (and takeappropriate action) as a function of how far outside the owner'spredicted cluster an attempted transaction falls. Here, for example, atleast one second factor authentication process may be initiated, if themapped cluster is too far from the expected cluster, to establish thatthe individual transacting with the POS device 195 is the known owner ofthe one or both of the card 110 and/or the mobile device 160 being usedin the transaction.

It is noted that the disclosed POS devices, systems, platforms, methods,and computer-readable media include or involve a fraud detectionmechanism that may include and/or involve a POS device 195 configured toperform various automated functionality set forth herein. Unlikeconventional software and solutions, the present innovations may utilizean improved POS device 195 that may, via obtaining the features andsensory inputs from the transaction card as well as the mapping of thesensory inputs to a cluster position of a plurality of clusters, beconfigured to detect fraud at the point-of-service, in the moment priorto authorizing the transaction. In these and other ways, implementationsinvolving the present POS devices 195 and associated features,functionality, and POS fraud detection mechanisms represent improvementsover existing fraud detection for transactions using cards, and/or cardsand mobile devices.

The disclosed mechanism of detecting fraud at point-of-service improvesutilization of both processing and communication resources. As aninitial matter, the present embodiments may store and map the cluster oflearned features from customers on the local POS device at the merchantsite. This obviates communication bandwidth otherwise used for networkaccess during the transaction to perform fraud detection, such as withprior systems, e.g., those that require contact with a financialinstitution to perform the fraud detection on the server side andtransmit instructions back to the POS device to authorize thetransaction. Such benefits are achieved by embodiments that includemapping one or more sensory inputs to a first cluster position,determining when the first cluster position of the at least oneparticular cluster mapped for the transaction matches a second cluster,and initiating at least one second factor authentication process whenthe first cluster position of the at least one particular cluster doesnot correspond to a second cluster position of the at least one expectedcluster. Further, because present embodiments need only perform astraightforward mapping of the identifier and/or feature informationagainst a local cluster, without involving any additional entities andcomputer systems outside of or beyond the POS device, the processing andcompute resources required are reduced substantially compared toexisting techniques for fraud detection performed for POS devicetransactions. Moreover, improved POS devices having the disclosed frauddetection mechanisms improve responsiveness, efficiency, accuracy,robustness, autonomousness and fault-tolerance ability of POS frauddetection. Implementations herein also reduce likelihood of merchantexposure to fraud involving cards and/or mobile devices, as well aslikelihood of “lag time” caused by communication or network intermittentavailability or failures, thereby reducing or eliminating the need forcommunicating with remote entities at the moment of the transaction tomake a fraud determination.

Turning back to FIG. 1, server 101 may include at least one processor102 and a memory 104, such as random-access memory (RAM). In someembodiments, server 101 may be operated by the financial institutionissuing the transaction card, by the merchant, and/or by any transactionclearing house used for authorizing the credit card for use.

Transaction card 110 may be formed from plastic, metal, or any othersuitable material. Transaction card 110 may include card circuitry 113formed directly therein, and/or disposed therein by gluing, bonding orby any suitable adhesion method for affixing circuitry to the materialof transaction card 110. Card circuitry 113 may be configured to utilizeany hardwired circuitry. Card circuitry 113 may be implemented as one ormore integrated circuit chips, and/or electronic devices, electricallyinterconnected and bonded to one or more circuit boards, for example.

Card circuitry 113 may include a memory 120, at least one processor 125,sensors and circuitry associated with acquiring the sensory inputs andinformation 130, authentication circuitry 127, communication circuitryand interface 140, and a power source 145. Memory 120 may store code,such as for the authentication circuitry 127, which when executed byprocessor 125 may cause processor 125 to implement assembling andtransmitting data to the POS device and/or other, related schemesherein, such as pairing with mobile device 160 to perform frauddetection, such as verifying if a user of transaction card 110 is anauthorized user of the card, e.g., to approve the transaction when theuser attempts to use the transaction card to purchase goods and/orservices at POS device 195.

In some embodiments, power source 145 may be used to power cardcircuitry 113. Power source 145 may include, for example, a battery, asolar cell, and/or any suitable energy harvesting device, capable ofgenerating enough power for powering card circuitry 113. In otherembodiments, the transaction card may be powered upon swiping orinserted the card into a slot in POS terminal 195 such that the powersource may be POS terminal 195 itself or any other device into which thetransaction card is swiped or inserted. The transaction card 110 mayalso be powered by movement, or by induction, or by other near-fieldelectromagnetic energy derived from nearby sources, such as mobiledevice 160, POS device 195, or other known sources. Once powered, thetransaction card may begin assembling the sensory data and/or featureset for communication to the POS terminal 195.

Mobile device 160, such as a smart phone or other portable or wearableelectronic device, may include mobile device circuitry. Mobile devicecircuitry may include a mobile device processor, a memory, such as RAM,communication circuitry and interface, and any input and/or outputdevice, such as a touchscreen display. RAM may store code that, whenexecuted by processor, may cause processor to implement aspects of oneor more fraud detections schemes herein, including those involvingpairing with transaction card 110 to verify if a user of the transactioncard 110 is an authorized user of the card. In some embodiments, anytransaction card application running on mobile device 160, such as anapplication supplied by the financial institution issuing thetransaction card and/or managing the transactions of the transactioncard user, may include various modules that may transmit information tothe POS device, relay information back to the financial institution(e.g., server 101), and communicate with other computing components.

Various embodiments associated with FIG. 1 and related disclosure hereinsolve a technical problem of ensuring that a transaction card is onlyused by the authorized user of the transaction card, e.g., the accountowner. Various features and functionality disclosed herein may beutilized in connection with fraud detection and/or authenticationprocesses that involve pairing of transaction card 110 with mobiledevice 160 when implementing multi-factor authentication (MFA) schemes,for example to authorize the card for use by the user. In otherembodiments, various information related to the successful pairing ofthe transaction card and the mobile device may be relayed back to server101 (e.g., server processor 102) so as to approve transactions forpurchasing goods and/or services with the authorized user's transactioncard.

In some embodiments, an initial authentication for pairing thetransaction card with the mobile device may be implemented by the usercontacting the financial institution from the user's mobile device toinitially authorize the pairing of transaction card 110 with mobiledevice 160 so as to receive pairing approval. In other embodiments, thepairing and/or unpair processes between the transaction card and themobile device may occur automatically and seamlessly such as without anyaction on the part of the user, particularly if the same mobile devicehad been previously paired with the same transaction card in the past.In yet other embodiments, proximity MFA may use biometrics (e.g.,fingerprint, voice recognition, etc.) and/or a password entered by theuser and/or a swiping of the mobile device screen by a finger of theuser and/or the proximity of the transaction card to the mobile deviceor any client device, for example, to pair or unpair the transactioncard with the client.

In some embodiments, when the transaction card may include a battery aspower source 145, the transaction card and the mobile device may beconfigured to pair with the transaction card on the fly when thetransaction card is used during a transaction, so as to conserve powerstored in the battery.

In some embodiments, if the transaction card is determined to bepotentially in use by an unauthorized user using the embodiments taughtherein, e.g. at or via POS device 195, the merchant using POS terminal195, may generate or receive an alarm or alert that the user ispotentially unauthorized (e.g., an alert on a display of POS terminal195) or that additional authentication, such as second-factorauthentication, should be performed to verify that the transaction isnot fraudulent.

FIG. 2 shows a diagram of an exemplary transaction card 110, consistentwith disclosed embodiments. In some embodiments, transaction card 110may be the approximate size and shape of a traditional credit card,debit card, or the like. Transaction card 110 may have embeddedelectronics for performing various aspects of the disclosed innovations.As shown, transaction card 110 may include at least one processor 210 orprocessing circuitry, memory 230, power source or power circuitry 240,one or more sensors 250, communication circuitry or devices 255,(optional) biometric input elements and/or circuitry 260, a magneticstripe 280, and other coupling circuitry 220 such as an electronic chipelement. Power source or power circuitry 240 may include elements thatgenerate power for the card upon coupling to a POS device, such as byconnection via an electronic chip, and/or such circuitry may include avoltage supply such as a battery. In some embodiments, transaction card110 may include more or fewer components than shown in FIG. 2.

Processor 210 may comprise one or more known or specialized processingdevices, of sufficient size and form factor to fit within transactioncard 110 when configured to be about the size of a traditional credit ordebit card. In some embodiments, processor 210 may include anyconfiguration capable of performing functions related to the disclosedmethods such as, for example, generating and transmitting theidentifier, feature set, and/or sensory data associated with usage ofthe transaction card 110, which may be based on the various sensoryinputs 250, 260 generated by the transaction card 110. Processor 210 mayalso control power source 240, send and receive data, read from andwritten to memory 230, receive and analyze data from sensors 250,process information or instructions associated with the couplingcircuitry 220, receive and process input from the biometric inputelements and/or circuitry 260, and any other functions consistent withthe disclosed embodiments.

Memory 230 may include volatile or non-volatile, magnetic,semiconductor, or other type of storage elements and/or tangible (i.e.,non-transitory) computer-readable medium that stores relevant data, suchas information needed for or associated with conducting cardtransactions. With regard to the data generated by the card for mappingagainst the cluster stored in the POS device, such data may be stored asa set of features, which may be encrypted or otherwise secured, and/orit may be transformed, e.g., by hash, fuzzy-hash, etc., into anidentifier, with the identifier being stored in memory 230 fortransmission to the POS device. Data of prior features and/oridentifiers may also be stored in the memory, 230, wherein a historicallist of such data may be maintained and used for determining the currentcluster in which the owner of the transaction card is to be assigned. Inone exemplary implementation, the historical data (feature sets)regarding a card owner's prior transactions are stored in memory, anduploaded periodically to the server 101, so that the server 101 mayconfirm or recalculate the cluster to which the owner's expectedbehavior is assigned. According to embodiments herein, the memory 230may also store user information, data needed or used by the card or thePOS device to achieve the innovations herein, other computer-executableinstructions and/or data known in the art.

Power source 240 may include a power storage device such as a battery orcapacitor, a power receiver such as an inductive power coil or awireless power receiver, a power generator such as a solar or kineticpower generator, or any combination thereof. In some embodiments, powersource 240 may include one or more other known devices capable ofgenerating, receiving, and/or storing electrical energy.

One or more sensors 250 may include one or more devices capable ofsensing the environment around transaction card 110, movement of thetransaction card 110, and/or other detectable interactions involving thetransaction card 110. In some embodiments, such sensors 250 may include,for example, one or more of a camera, a microphone, a gyroscope, anaccelerometer, a shock sensor, a position sensor, a light sensor such asan ambient light sensor, a temperature sensor, a touch sensor, aconductivity sensor, and/or a haptic sensor.

Sensors 250 may also include one or more buttons, switches, othertactile input mechanisms, or other forms of user-derived input forreceiving an indication or instruction from a card user. In someembodiments, such input devices may receive a sequence or series ofinputs, to cause processor 210 to perform various functions associatedwith the disclosed embodiments.

Further, while shown separately at 260, sensory inputs may also beobtained via the biometric input elements and/or circuitry 260. In someembodiments, such biometric input elements and/or circuitry 260 mayinclude, for example, one or more of a fingerprint sensor, an opticalsensor that detects one or more of a card user's face, eyes, or otherdistinguishing features of the card user, and/or a sensor that detectselectromagnetic energy emitted from the card user.

The transaction card may, optionally, also include a display, which maycomprise a screen, indicator light, or other appropriate device fordisplaying a status or message to user. In some embodiments, display mayinclude a small LCD screen, e-ink screen, or OLED display or one or moreLEDs. In some embodiments, display may provide notifications, prompts,and/or messages to user.

In some embodiments, transaction card 110 may include communicationcircuitry or devices 255 such as antennae and/or NFC (near-fieldcommunication) circuitry, for transmitting and/or receiving data fromone or more external locations. Communication circuitry 255 may comprisea short-range wireless transceiver, or a near-field communication chip.Communication circuitry 255 may be configured to communicate with mobiledevice 160, a contactless card reader associated with the POS device195, other systems, and/or other sensors configured to detect thepresence of transaction card 110.

In some embodiments, transaction card 110 may include at least onemagnetic stripe 280 or other magnetic communication medium that mayshare or read magnetically-stored information. In some embodiments,magnetic stripe 280 may be controlled by processor 210. For example,processor 210 may write, clear, and rewrite magnetic stripe 280, toprovide particular account information.

FIG. 3 is a flowchart illustrating one exemplary process 300 related tofraud detection at a POS device, consistent with exemplary aspects of atleast some embodiments of the present disclosure. As shown in theexemplary flowchart of FIG. 3, an illustrative fraud detection process300 may comprise: obtaining, from a transaction card by a POS device,upon initiation of a transaction involving: (1) a card, or (2) the cardand a mobile device associated with an individual transacting with thePOS device, one or more sensory inputs and/or an identifier, at 302;mapping, by the POS device, the one or more sensory inputs to a firstcluster position of at least one particular cluster of a plurality ofclusters, at 304; determining whether the first cluster position of theat least one particular cluster mapped for the transaction correspondsto a second cluster position of the at least one expected clusterassociated with the known owner of the one or both of the card andmobile device, at 306; and initiating at least one second factorauthentication process to establish that the individual transacting withthe POS device is the known owner of one or both of the card and themobile device being used in the transaction, at 308. Further, the frauddetection process 300 may be carried out by and at the POS device, asset forth in more detail elsewhere herein.

As noted above, the fraud detection process 300 may include, at 302, astep of obtaining one or more sensory inputs and/or an identifier, by apoint-of-service (POS) device, upon initiation of a transactioninvolving: (1) a card, or (2) the card and a mobile device associatedwith an individual transacting with the POS device. With regard to thedisclosed innovations, the one or more sensory inputs may include one ormore sensory inputs associated with a use of the one or both of the cardand the mobile device for the transaction. The sensory inputs may alsobe combined with other data and provided as a feature set. In addition,an identifier that serves to provide a summary or secure/encryptedversion of such sensory data may also be provide along with, or in placeof, the sensory inputs. Further, such identifier may be configured tobe: (1) associated with a known owner of one or both of the card and themobile device; and/or (2) used to determine whether the one or moresensory inputs are consistent with the prior transaction behavior of theowner. Here, for example, the identifier may be a hashed or otherwisetransformed value that corresponds to the feature set of informationthat the transaction card has assembled leading up to the transactionand transmitted to the POS device. In some embodiments the feature setmay comprise the sensory inputs gathered by the transaction card and/orvarious static, fixed, and/or physical features associated thetransaction card (e.g., name on card, type of card, material of card,CCV code, size of card, etc.) and/or related electronic informationregarding the transaction card or owner that is difficult for fraudulentcards to emulate.

According to embodiments herein, in addition to sensor data, inputsprocessed regarding an attempted transaction may also include one ormore of: (i) physical features of the card including one or more of: thedimensions of the card (e.g., height, width, depth, shape, etc.), thename on the card, material and/or color of the card, embossing,inscriptions and/or any other visual or tactile characteristics of thecard; (ii) the location of the card, and/or the movement of the card,and/or the location of the mobile device, and/or the movement of themobile device; and (iii) the biometric or other information regardingthe individual transacting with the card, and/or the biometric or otherinformation regarding the individual transacting with the mobile device.

As shown in FIG. 3, the fraud detection process 300 may include, at 304,a step of mapping, by the POS device, the one or more sensory inputs toa first cluster position of at least one particular cluster of aplurality of clusters. According to various disclosed embodiments, theplurality of clusters may be configured to define sets of hashed,learned features regarding prior known interactions of the owners ofcards with POS devices. In some embodiments, the prior knowninteractions may be mapped by machine learning techniques into theplurality of clusters. In implementations, the prior known interactionsmay be mapped by machine learning techniques into the plurality ofclusters such that prior sensory input of each owner has been mapped toat least one expected cluster.

Further, in some embodiments, the plurality of clusters may be providedto the POS device via one or both of a smart card (SIM card) and othercomputer-readable media. Here, for example, one or both of such smartcard and such other computer-readable media may be provided from anentity associated with the one or both of the card and the mobiledevice. In various implementations, such an entity may be involved withpreventing fraudulent transactions. In one embodiment, the plurality ofclusters may be provided to the POS device via download from one or moreservers associated with at least one entity involved in the preventionof fraudulent transactions.

Further, in some embodiments, the step of mapping the one or moresensory inputs to the first cluster position may comprise direct mappingof the one or more sensory inputs to the first cluster position. In someother embodiments, the step of mapping the one or more sensory inputs tothe first cluster position may comprise generating a feature set ormodel of the one or more sensory inputs, and mapping the feature set ormodel to the first cluster position.

Exemplary fraud detection process 300 may also include, at 306, a stepof determining whether the first cluster position of the at least oneparticular cluster mapped for the transaction corresponds to a secondcluster position of the at least one expected cluster associated withthe known owner of the one or both of the card and mobile device.According to various disclosed embodiments, step 306 may be performed bythe POS device. Here, for example, some embodiments may be configured todetermine when the cluster position of the at least one particularcluster mapped for the transaction matches the second cluster positionat the POS device, such that a fraud determination may be performedon-site at the POS device. In some implementations, such frauddetermination may be performed without the need for communicating withremote entities to make the fraud determination.

Exemplary fraud detection process 300 may further include, at 308, astep of initiating at least one second factor authentication process toestablish that the individual transacting with the POS device is theknown owner of the card and/or the mobile device being used in thetransaction. According to various embodiments, step 308 may be performedby the POS device. Various embodiments herein may be configured toinitiate such second factor authentication process when the firstcluster position of the at least one particular cluster does notcorrespond to the second cluster position of the at least one expectedcluster. In some embodiments, the second factor authentication may betriggered via processing performed by the POS device, and the secondfactor authentication may be performed at the POS device.

Further, according to some embodiments, the second factor authenticationprocess may comprise generating an alert to a merchant associated withthe POS device. In other embodiments, the second factor authenticationprocess may comprise generating an alert to a financial services entityassociated with one or more of the POS device, the card, and/or theowner of the mobile device.

According to some embodiments, illustrative methods herein may furthercomprise: receiving the one or more sensory inputs; utilizing a machinelearning algorithm to generate, based on the one or more sensory inputs,a feature set representing the one or more sensory inputs; and hashingthe feature set to obtain an expected cluster. Embodiments herein may beconfigured such that one or more of these steps may be performed by themobile device 160. In some implementations, the feature set may behashed to obtain the expected cluster that is specific to an individualassociated with the mobile device 160. Here, for example, the machinelearning algorithm may be specifically configured to an individualassociated with the mobile device 160.

Further, according to some other embodiments, methods herein may alsocomprise authorizing the transaction when the first cluster position ofthe at least one particular cluster does correspond to the secondcluster position of the at least one expected cluster. In variousimplementations, the step of authorizing as described above may beperformed by the POS device.

FIG. 4 depicts a block diagram of an exemplary computer-basedsystem/platform in accordance with one or more embodiments of thepresent disclosure. However, not all of these components may be requiredto practice one or more embodiments, and variations in the arrangementand type of the components may be made without departing from the spiritor scope of various embodiments of the present disclosure. In someembodiments, the exemplary inventive computing devices and/or theexemplary inventive computing components of the exemplary computer-basedsystem/platform may be configured to manage a large number of instancesof software applications, users, and/or concurrent transactions, asdetailed herein. In some embodiments, the exemplary computer-basedsystem/platform may be based on a scalable computer and/or networkarchitecture that incorporates varies strategies for assessing the data,caching, searching, and/or database connection pooling. An example ofthe scalable architecture is an architecture that is capable ofoperating multiple servers.

In some embodiments, referring to FIG. 4, members 702-704 (e.g., POSdevices or clients) of the exemplary computer-based system/platform mayinclude virtually any computing device capable of receiving and sendinga message over a network (e.g., cloud network), such as network 705, toand from another computing device, such as servers 706 and 707, eachother, and the like. In some embodiments, the member devices 702-704 maybe personal computers, multiprocessor systems, microprocessor-based orprogrammable consumer electronics, network PCs, and the like. In someembodiments, one or more member devices within member devices 702-704may include computing devices that typically connect using a wirelesscommunications medium such as cell phones, smart phones, pagers, walkietalkies, radio frequency (RF) devices, infrared (IR) devices, CBs,integrated devices combining one or more of the preceding devices, orvirtually any mobile computing device, and the like. In someembodiments, one or more member devices within member devices 702-704may be devices that are capable of connecting using a wired or wirelesscommunication medium such as a PDA, POCKET PC, wearable computer, alaptop, tablet, desktop computer, a netbook, a video game device, apager, a smart phone, an ultra-mobile personal computer (UMPC), and/orany other device that is equipped to communicate over a wired and/orwireless communication medium (e.g., NFC, RFID, NBIOT, 3G, 4G, 5G, GSM,GPRS, WiFi, WiMax, CDMA, satellite, ZigBee, etc.). In some embodiments,one or more member devices within member devices 702-704 may include mayrun one or more applications, such as Internet browsers, mobileapplications, voice calls, video games, videoconferencing, and email,among others. In some embodiments, one or more member devices withinmember devices 702-704 may be configured to receive and to send webpages, and the like. In some embodiments, an exemplary specificallyprogrammed browser application of the present disclosure may beconfigured to receive and display graphics, text, multimedia, and thelike, employing virtually any web based language, including, but notlimited to Standard Generalized Markup Language (SMGL), such asHyperText Markup Language (HTML), a wireless application protocol (WAP),a Handheld Device Markup Language (HDML), such as Wireless MarkupLanguage (WML), WMLScript, XML, JavaScript, and the like. In someembodiments, a member device within member devices 702-704 may bespecifically programmed by either Java, .Net, QT, C, C++ and/or othersuitable programming language. In some embodiments, one or more memberdevices within member devices 702-704 may be specifically programmedinclude or execute an application to perform a variety of possibletasks, such as, without limitation, messaging functionality, browsing,searching, playing, streaming or displaying various forms of content,including locally stored or uploaded messages, images and/or video,and/or games.

In some embodiments, the exemplary network 705 may provide networkaccess, data transport and/or other services to any computing devicecoupled to it. In some embodiments, the exemplary network 705 mayinclude and implement at least one specialized network architecture thatmay be based at least in part on one or more standards set by, forexample, without limitation, GlobalSystem for Mobile communication (GSM)Association, the Internet Engineering Task Force (IETF), and theWorldwide Interoperability for Microwave Access (WiMAX) forum. In someembodiments, the exemplary network 705 may implement one or more of aGSM architecture, a General Packet Radio Service (GPRS) architecture, aUniversal Mobile Telecommunications System (UMTS) architecture, and anevolution of UMTS referred to as Long Term Evolution (LTE). In someembodiments, the exemplary network 705 may include and implement, as analternative or in conjunction with one or more of the above, a WiMAXarchitecture defined by the WiMAX forum. In some embodiments and,optionally, in combination of any embodiment described above or below,the exemplary network 705 may also include, for instance, at least oneof a local area network (LAN), a wide area network (WAN), the Internet,a virtual LAN (VLAN), an enterprise LAN, a layer 3 virtual privatenetwork (VPN), an enterprise IP network, or any combination thereof. Insome embodiments and, optionally, in combination of any embodimentdescribed above or below, at least one computer network communicationover the exemplary network 705 may be transmitted based at least in parton one of more communication modes such as but not limited to: NFC,RFID, Narrow Band Internet of Things (NBIOT), ZigBee, 3G, 4G, 5G, GSM,GPRS, WiFi, WiMax, CDMA, satellite and any combination thereof. In someembodiments, the exemplary network 705 may also include mass storage,such as network attached storage (NAS), a storage area network (SAN), acontent delivery network (CDN) or other forms of computer- ormachine-readable media.

In some embodiments, the exemplary server 706 or the exemplary server707 may be a web server (or a series of servers) running a networkoperating system, examples of which may include but are not limited toMicrosoft Windows Server, Novell NetWare, or Linux. In some embodiments,the exemplary server 706 or the exemplary server 707 may be used forand/or provide cloud and/or network computing. Although not shown inFIG. 4, in some embodiments, the exemplary server 706 or the exemplaryserver 707 may have connections to external systems like email, SMSmessaging, text messaging, ad content providers, etc. Any of thefeatures of the exemplary server 706 may be also implemented in theexemplary server 707 and vice versa.

In some embodiments, one or more of the exemplary servers 706 and 707may be specifically programmed to perform, in non-limiting example, asauthentication servers, search servers, email servers, social networkingservices servers, SMS servers, IM servers, MMS servers, exchangeservers, photo-sharing services servers, advertisement providingservers, financial/banking-related services servers, travel servicesservers, or any similarly suitable service-base servers for users of themember computing devices 701-704.

In some embodiments and, optionally, in combination of any embodimentdescribed above or below, for example, one or more exemplary computingmember devices 702-704, the exemplary server 706, and/or the exemplaryserver 707 may include a specifically programmed software module thatmay be configured to send, process, and receive information using ascripting language, a remote procedure call, an email, a tweet, ShortMessage Service (SMS), Multimedia Message Service (MMS), instantmessaging (IM), internet relay chat (IRC), mIRC, Jabber, an applicationprogramming interface, Simple Object Access Protocol (SOAP) methods,Common Object Request Broker Architecture (CORBA), HTTP (HypertextTransfer Protocol), REST (Representational State Transfer), or anycombination thereof.

FIG. 5 depicts a block diagram of another exemplary computer-basedsystem/platform 800 in accordance with one or more embodiments of thepresent disclosure. However, not all of these components may be requiredto practice one or more embodiments, and variations in the arrangementand type of the components may be made without departing from the spiritor scope of various embodiments of the present disclosure. In someembodiments, the member computing devices (e.g., POS devices), i.e. 802a and 802 b through 802 n, each at least includes a computer-readablemedium, such as a random-access memory (RAM) 808 coupled to a processor810 and/or memory 808. In some embodiments, the processor 810 mayexecute computer-executable program instructions stored in memory 808.In some embodiments, the processor 810 may include a microprocessor, anASIC, and/or a state machine. In some embodiments, the processor 810 mayinclude, or may be in communication with, media, for examplecomputer-readable media, which stores instructions that, when executedby the processor 810, may cause the processor 810 to perform one or moresteps described herein. In some embodiments, examples ofcomputer-readable media may include, but are not limited to, anelectronic, optical, magnetic, or other storage or transmission devicecapable of providing a processor, such as the processor 810 of client802 a, with computer-readable instructions. In some embodiments, otherexamples of suitable media may include, but are not limited to, a floppydisk, CD-ROM, DVD, magnetic disk, memory chip, ROM, RAM, an ASIC, aconfigured processor, all optical media, all magnetic tape or othermagnetic media, or any other medium from which a computer processor canread instructions. Also, various other forms of computer-readable mediamay transmit or carry instructions to a computer, including a router,private or public network, or other transmission device or channel, bothwired and wireless. In some embodiments, the instructions may comprisecode from any computer-programming language, including, for example, C,C++, Visual Basic, Java, Python, Perl, JavaScript, and etc.

In some embodiments, member computing devices 802 a through 802 n mayalso comprise a number of external or internal devices such as a mouse,a CD-ROM, DVD, a physical or virtual keyboard, a display, audio outputsuch as a speaker, or other input or output devices. In someembodiments, examples of member computing devices 802 a through 802 n(e.g., clients) may be any type of processor-based platforms that areconnected to a network 806 such as, without limitation, personalcomputers, digital assistants, personal digital assistants, smartphones, pagers, digital tablets, laptop computers, Internet appliances,and other processor-based devices. In some embodiments, member computingdevices 802 a through 802 n may be specifically programmed with one ormore application programs in accordance with one or moreprinciples/methodologies detailed herein. In some embodiments, membercomputing devices 802 a through 802 n may operate on any operatingsystem capable of supporting a browser or browser-enabled application,such as Microsoft™, Windows™, and/or Linux. In some embodiments, membercomputing devices 802 a through 802 n shown may include, for example,personal computers executing a browser application program such asMicrosoft Corporation's Internet Explorer™, Apple Computer, Inc.'sSafari™, Mozilla Firefox, and/or Opera. In some embodiments, through themember computing client devices (802 a through 802 n), users (812 athrough 812 n) may communicate over the exemplary network 806 with eachother and/or with other systems and/or devices coupled to the network806. As shown in FIG. 5, exemplary server devices 804 and 813 may bealso coupled to the network 806. In some embodiments, one or more membercomputing devices 802 a through 802 n may be mobile clients.

In some embodiments, at least one database of exemplary databases 807and 815 may be any type of database, including a database managed by adatabase management system (DBMS). In some embodiments, an exemplaryDBMS-managed database may be specifically programmed as an engine thatcontrols organization, storage, management, and/or retrieval of data inthe respective database. In some embodiments, the exemplary DBMS-manageddatabase may be specifically programmed to provide the ability to query,backup and replicate, enforce rules, provide security, compute, performchange and access logging, and/or automate optimization. In someembodiments, the exemplary DBMS-managed database may be chosen fromOracle database, IBM DB2, Adaptive Server Enterprise, FileMaker,Microsoft Access, Microsoft SQL Server, MySQL, PostgreSQL, and a NoSQLimplementation. In some embodiments, the exemplary DBMS-managed databasemay be specifically programmed to define each respective schema of eachdatabase in the exemplary DBMS, according to a particular database modelof the present disclosure which may include a hierarchical model,network model, relational model, object model, or some other suitableorganization that may result in one or more applicable data structuresthat may include fields, records, files, and/or objects. In someembodiments, the exemplary DBMS-managed database may be specificallyprogrammed to include metadata about the data that is stored.

As also shown in FIGS. 6 and 7, some embodiments of the disclosedtechnology may also include and/or involve one or more cloud components825, which are shown grouped together in the drawing for sake ofillustration, though may be distributed in various ways as known in theart. Cloud components 825 may include one or more cloud services such assoftware applications (e.g., queue, etc.), one or more cloud platforms(e.g., a Web front-end, etc.), cloud infrastructure (e.g., virtualmachines, etc.), and/or cloud storage (e.g., cloud databases, etc.).

According to some embodiments shown by way of one example in FIG. 7, theexemplary inventive computer-based systems/platforms, the exemplaryinventive computer-based devices, components and media, and/or theexemplary inventive computer-implemented methods of the presentdisclosure may be specifically configured to operate in or with cloudcomputing/architecture such as, but not limiting to: infrastructure aservice (IaaS) 1010, platform as a service (PaaS) 1008, and/or softwareas a service (SaaS) 1006. FIGS. 6 and 7 illustrate schematics ofexemplary implementations of the cloud computing/architecture(s) inwhich the exemplary inventive computer-based systems/platforms, theexemplary inventive computer-implemented methods, and/or the exemplaryinventive computer-based devices, components and/or media of the presentdisclosure may be specifically configured to operate. In someembodiments, such cloud architecture 1006, 1008, 1010 may be utilized inconnection with the Web browser and browser extension aspects, shown at1004, to achieve the innovations herein.

As used in the description and in any claims, the term “based on” is notexclusive and allows for being based on additional factors notdescribed, unless the context clearly dictates otherwise. In addition,throughout the specification, the meaning of “a,” “an,” and “the”include plural references. The meaning of “in” includes “in” and “on.”

As used herein, the terms “and” and “or” may be used interchangeably torefer to a set of items in both the conjunctive and disjunctive in orderto encompass the full description of combinations and alternatives ofthe items. By way of example, a set of items may be listed with thedisjunctive “or”, or with the conjunction “and.” In either case, the setis to be interpreted as meaning each of the items singularly asalternatives, as well as any combination of the listed items.

It is understood that at least one aspect/functionality of variousembodiments described herein can be performed in real-time and/ordynamically. As used herein, the term “real-time” is directed to anevent/action that can occur instantaneously or almost instantaneously intime when another event/action has occurred. For example, the “real-timeprocessing,” “real-time computation,” and “real-time execution” allpertain to the performance of a computation during the actual time thatthe related physical process (e.g., a user interacting with anapplication on a mobile device) occurs, in order that results of thecomputation can be used in guiding the physical process.

As used herein, the term “dynamically” and term “automatically,” andtheir logical and/or linguistic relatives and/or derivatives, mean thatcertain events and/or actions can be triggered and/or occur without anyhuman intervention. In some embodiments, events and/or actions inaccordance with the present disclosure can be in real-time and/or basedon a predetermined periodicity of at least one of: nanosecond, severalnanoseconds, millisecond, several milliseconds, second, several seconds,minute, several minutes, hourly, several hours, daily, several days,weekly, monthly, etc.

As used herein, the term “runtime” corresponds to any behavior that isdynamically determined during an execution of a software application orat least a portion of software application.

In some embodiments, exemplary inventive, specially programmed computingsystems/platforms with associated devices are configured to operate inthe distributed network environment, communicating with one another overone or more suitable data communication networks (e.g., the Internet,satellite, etc.) and utilizing one or more suitable data communicationprotocols/modes such as, without limitation, IPX/SPX, X.25, AX.25,AppleTalk™, TCP/IP (e.g., HTTP), Bluetooth™, near-field wirelesscommunication (NFC), RFID, Narrow Band Internet of Things (NBIOT), 3G,4G, 5G, GSM, GPRS, WiFi, WiMax, CDMA, satellite, ZigBee, and othersuitable communication modes. Various embodiments herein may includeinteractive posters that involve wireless, e.g., Bluetooth™ and/or NFC,communication aspects, as set forth in more detail further below. Insome embodiments, the NFC can represent a short-range wirelesscommunications technology in which NFC-enabled devices are “swiped,”“bumped,” “tap” or otherwise moved in close proximity to communicate. Insome embodiments, the NFC could include a set of short-range wirelesstechnologies, typically requiring a distance of 10 cm or less. In someembodiments, the NFC may operate at 13.56 MHz on ISO/IEC 18000-3 airinterface and at rates ranging from 106 kbit/s to 424 kbit/s. In someembodiments, the NFC can involve an initiator and a target; theinitiator actively generates an RF field that can power a passivetarget. In some embodiments, this can enable NFC targets to take verysimple form factors such as tags, stickers, key fobs, or cards that donot require batteries. In some embodiments, the NFC's peer-to-peercommunication can be conducted when a plurality of NFC-enable devices(e.g., smartphones) are within close proximity of each other.

The material disclosed herein may be implemented in software or firmwareor a combination of them or as instructions stored on a machine-readablemedium, which may be read and executed by one or more processors. Amachine-readable medium may include any medium and/or mechanism forstoring or transmitting information in a form readable by a machine(e.g., a computing device). For example, a machine-readable medium mayinclude read only memory (ROM); random access memory (RAM); magneticdisk storage media; optical storage media; flash memory devices;electrical, optical, acoustical or other forms of propagated signals(e.g., carrier waves, infrared signals, digital signals, etc.), andothers.

As used herein, the terms “computer engine” and “engine” identify atleast one software component and/or a combination of at least onesoftware component and at least one hardware component which aredesigned/programmed/configured to manage/control other software and/orhardware components (such as the libraries, software development kits(SDKs), objects, etc.). Computer-related systems, computer systems, andsystems, as used herein, include any combination of hardware andsoftware.

Examples of hardware elements may include processors, microprocessors,circuits, circuit elements (e.g., transistors, resistors, capacitors,inductors, and so forth), integrated circuits, application specificintegrated circuits (ASIC), programmable logic devices (PLD), digitalsignal processors (DSP), field programmable gate array (FPGA), logicgates, registers, semiconductor device, chips, microchips, chip sets,and so forth. In some embodiments, the one or more processors may beimplemented as a Complex Instruction Set Computer (CISC) or ReducedInstruction Set Computer (RISC) processors; x86 instruction setcompatible processors, multi-core, or any other microprocessor orcentral processing unit (CPU). In various implementations, the one ormore processors may be dual-core processor(s), dual-core mobileprocessor(s), and so forth.

Examples of software may include software components, programs,applications, operating system software, middleware, firmware, softwaremodules, routines, subroutines, functions, methods, procedures, softwareinterfaces, application program interfaces (API), instruction sets,computer code, computer code segments, words, values, symbols, or anycombination thereof. Determining whether an embodiment is implementedusing hardware elements and/or software elements may vary in accordancewith any number of factors, such as desired computational rate, powerlevels, heat tolerances, processing cycle budget, input data rates,output data rates, memory resources, data bus speeds and other design orperformance constraints.

One or more aspects of at least one embodiment may be implemented byrepresentative instructions stored on a machine-readable medium whichrepresents various logic within the processor, which when read by amachine causes the machine to fabricate logic to perform the techniquesdescribed herein. Such representations, known as “IP cores” may bestored on a tangible, machine readable medium and supplied to variouscustomers or manufacturing facilities to load into the fabricationmachines that make the logic or processor. Of note, various embodimentsdescribed herein may, of course, be implemented using any appropriatehardware and/or computing software languages (e.g., C++, Objective-C,Swift, Java, JavaScript, Python, Perl, QT, etc.).

In some embodiments, one or more of exemplary inventive computer-basedsystems/platforms, exemplary inventive computer-based devices, and/orexemplary inventive computer-based components of the present disclosuremay include or be incorporated, partially or entirely into at least onepersonal computer (PC), laptop computer, ultra-laptop computer, tablet,touch pad, portable computer, handheld computer, palmtop computer,personal digital assistant (PDA), cellular telephone, combinationcellular telephone/PDA, television, smart device (e.g., smart phone,smart tablet or smart television), mobile internet device (MID),messaging device, data communication device, and so forth.

As used herein, the term “server” should be understood to refer to aservice point which provides processing, database, and communicationfacilities. By way of example, and not limitation, the term “server” canrefer to a single, physical processor with associated communications anddata storage and database facilities, or it can refer to a networked orclustered complex of processors and associated network and storagedevices, as well as operating software and one or more database systemsand application software that support the services provided by theserver. Cloud components (e.g., FIGS. 3-4) and cloud servers areexamples.

In some embodiments, as detailed herein, one or more of exemplaryinventive computer-based systems/platforms, exemplary inventivecomputer-based devices, and/or exemplary inventive computer-basedcomponents of the present disclosure may obtain, manipulate, transfer,store, transform, generate, and/or output any digital object and/or dataunit (e.g., from inside and/or outside of a particular application) thatcan be in any suitable form such as, without limitation, a file, acontact, a task, an email, a tweet, a map, an entire application (e.g.,a calculator), etc. In some embodiments, as detailed herein, one or moreof exemplary inventive computer-based systems/platforms, exemplaryinventive computer-based devices, and/or exemplary inventivecomputer-based components of the present disclosure may be implementedacross one or more of various computer platforms such as, but notlimited to: (1) AmigaOS, AmigaOS 4; (2) FreeBSD, NetBSD, OpenBSD; (3)Linux; (4) Microsoft Windows; (5) OpenVMS; (6) OS X (Mac OS); (7) OS/2;(8) Solaris; (9) Tru64 UNIX; (10) VM; (11) Android; (12) Bada; (13)BlackBerry OS; (14) Firefox OS; (15) Ios; (16) Embedded Linux; (17) PalmOS; (18) Symbian; (19) Tizen; (20) WebOS; (21) Windows Mobile; (22)Windows Phone; (23) Adobe AIR; (24) Adobe Flash; (25) Adobe Shockwave;(26) Binary Runtime Environment for Wireless (BREW); (27) Cocoa (API);(28) Cocoa Touch; (29) Java Platforms; (30) JavaFX; (31) JavaFX Mobile;(32) Microsoft XNA; (33) Mono; (34) Mozilla Prism, XUL and XULRunner;(35) .NET Framework; (36) Silverlight; (37) Open Web Platform; (38)Oracle Database; (39) Qt; (40) SAP NetWeaver; (41) Smartface; (42) Vexi;and/OR (43) Windows Runtime.

In some embodiments, exemplary inventive computer-basedsystems/platforms, exemplary inventive computer-based devices, and/orexemplary inventive computer-based components of the present disclosuremay be configured to utilize hardwired circuitry that may be used inplace of or in combination with software instructions to implementfeatures consistent with principles of the disclosure. Thus,implementations consistent with principles of the disclosure are notlimited to any specific combination of hardware circuitry and software.For example, various embodiments may be embodied in many different waysas a software component such as, without limitation, a stand-alonesoftware package, a combination of software packages, or it may be asoftware package incorporated as a “tool” in a larger software product.

For example, exemplary software specifically programmed in accordancewith one or more principles of the present disclosure may bedownloadable from a network, for example, a website, as a stand-aloneproduct or as an add-in package for installation in an existing softwareapplication. For example, exemplary software specifically programmed inaccordance with one or more principles of the present disclosure mayalso be available as a client-server software application, or as aweb-enabled software application. For example, exemplary softwarespecifically programmed in accordance with one or more principles of thepresent disclosure may also be embodied as a software package installedon a hardware device.

In some embodiments, exemplary inventive computer-basedsystems/platforms, exemplary inventive computer-based devices, and/orexemplary inventive computer-based components of the present disclosuremay be configured to output to distinct, specifically programmedgraphical user interface implementations of the present disclosure(e.g., a desktop, a web app., etc.). In various implementations of thepresent disclosure, a final output may be displayed on a displayingscreen which may be, without limitation, a screen of a computer, ascreen of a mobile device, or the like. In various implementations, thedisplay may be a holographic display. In various implementations, thedisplay may be a transparent surface that may receive a visualprojection. Such projections may convey various forms of information,images, and/or objects. For example, such projections may be a visualoverlay for a mobile augmented reality (MAR) application.

In some embodiments, exemplary inventive computer-basedsystems/platforms, exemplary inventive computer-based devices, and/orexemplary inventive computer-based components of the present disclosuremay be configured to be utilized in various applications which mayinclude, but not limited to, gaming, mobile-device games, video chats,video conferences, live video streaming, video streaming and/oraugmented reality applications, mobile-device messenger applications,and others similarly suitable computer-device applications.

As used herein, the term “mobile electronic device,” or the like, mayrefer to any portable electronic device that may or may not be enabledwith location tracking functionality (e.g., MAC address, InternetProtocol (IP) address, or the like). For example, a mobile electronicdevice can include, but is not limited to, a mobile phone, PersonalDigital Assistant (PDA), Blackberry™, Pager, Smartphone, smart watch, orany other reasonable mobile electronic device.

As used herein, the terms “proximity detection,” “locating,” “locationdata,” “location information,” and “location tracking” refer to any formof location tracking technology or locating method that can be used toprovide a location of, for example, a particular computingdevice/system/platform of the present disclosure and/or any associatedcomputing devices, based at least in part on one or more of thefollowing techniques/devices, without limitation: accelerometer(s),gyroscope(s), Global Positioning Systems (GPS); GPS accessed usingBluetooth™; GPS accessed using any reasonable form of wireless and/ornon-wireless communication; WiFi™ server location data; Bluetooth™ basedlocation data; triangulation such as, but not limited to, network basedtriangulation, WiFi™ server information based triangulation, Bluetooth™server information based triangulation; Cell Identification basedtriangulation, Enhanced Cell Identification based triangulation,Uplink-Time difference of arrival (U-TDOA) based triangulation, Time ofarrival (TOA) based triangulation, Angle of arrival (AOA) basedtriangulation; techniques and systems using a geographic coordinatesystem such as, but not limited to, longitudinal and latitudinal based,geodesic height based, Cartesian coordinates based; Radio FrequencyIdentification such as, but not limited to, Long range RFID, Short rangeRFID; using any form of RFID tag such as, but not limited to active RFIDtags, passive RFID tags, battery assisted passive RFID tags; or anyother reasonable way to determine location. For ease, at times the abovevariations are not listed or are only partially listed; this is in noway meant to be a limitation.

As used herein, the terms “cloud,” “Internet cloud,” “cloud computing,”“cloud architecture,” and similar terms correspond to at least one ofthe following: (1) a large number of computers connected through areal-time communication network (e.g., Internet); (2) providing theability to run a program or application on many connected computers(e.g., physical machines, virtual machines (VMs)) at the same time; (3)network-based services, which appear to be provided by real serverhardware, and are in fact served up by virtual hardware (e.g., virtualservers), simulated by software running on one or more real machines(e.g., allowing to be moved around and scaled up (or down) on the flywithout affecting the end user).

The aforementioned examples are, of course, illustrative and notrestrictive.

As used herein, the term “user” shall have a meaning of at least oneuser. In some embodiments, the terms “user”, “subscriber”, “consumer”,or “customer” should be understood to refer to a user of an applicationor applications as described herein and/or a consumer of data suppliedby a data provider. By way of example, and not limitation, the terms“user” or “subscriber” can refer to a person who receives data providedby the data or service provider over the Internet in a browser session,or can refer to an automated software application which receives thedata and stores or processes the data.

At least some aspects of the present disclosure will now be describedwith reference to the following numbered clauses.

Clause 1. A method comprising:

-   -   obtaining, by a point-of-service (POS) device, upon initiation        of a transaction involving (1) a card or (2) the card and a        mobile device associated with an individual transacting with the        POS device:        -   (i) one or more sensory inputs associated with a use of the            one or both of the card and the mobile device for the            transaction; and        -   (ii) an identifier (1) associated with a known owner of the            one or both of the card and the mobile device and (2) used            to determine whether the one or more sensory inputs are            consistent with prior transaction behavior of the owner;    -   mapping, by the POS device, the one or more sensory inputs to a        first cluster position of at least one particular cluster of a        plurality of clusters,        -   wherein the plurality of clusters defines sets of hashed,            learned features regarding prior known interactions of            owners of cards with POS devices, the prior known            interactions being mapped by machine learning into the            plurality of clusters such that prior sensory input of each            owner has been mapped to at least one expected cluster;    -   determining, by the POS device, when the cluster position of the        at least one particular cluster mapped for the transaction        matches a second cluster position of the at least one expected        cluster associated with the known owner of the one or both of        the card and mobile device; and    -   initiating, by the POS device, at least one second factor        authentication process to establish that the individual        transacting with the POS device is the known owner of the one or        both of the card and the mobile device being used in the        transaction when the first cluster position of the at least one        particular cluster does not correspond to the second cluster        position of the at least one expected cluster.        Clause 2. The method of clause 1 or of any clause herein,        wherein the plurality of clusters are provided to the POS device        via a smart card (SIM card) or other computer-readable medium        that is provided from an entity associated with the one or both        of the card and the mobile device and/or an entity involved with        preventing fraudulent transactions.        Clause 3. The method of clause 1 or of any clause herein,        wherein the plurality of clusters are provided to the POS device        via download from one or more servers associated with at least        one entity involved in the prevention of fraudulent        transactions.        Clause 4. The method of clause 1 or of any clause herein,        wherein the second factor authentication is triggered via        processing performed by the POS device, and the second factor        authentication is performed at the POS device.        Clause 5. The method of clause 1 or of any clause herein,        wherein the determining when the cluster position of the at        least one particular cluster mapped for the transaction matches        the second cluster position is performed at the POS device, such        that a fraud determination may be performed on-site at the POS        device without need for communicating with remote entities to        make the fraud determination.        Clause 6. The method of clause 1 or of any clause herein,        wherein the one or more inputs comprise one or more of:    -   (i) features of the card including one or more of dimension(s)        of card, name on card, and/or material and/or color of card;    -   (ii) location and/or movement of the card and/or the mobile        device; and/or    -   (iii) biometric or other information regarding the individual        transacting with the card and/or mobile device.        Clause 7. The method of clause 1 or of any clause herein,        wherein mapping the one or more sensory inputs to the first        cluster position comprises direct mapping of the one or more        sensory inputs to the first cluster position.        Clause 8. The method of clause 1 or of any clause herein,        wherein mapping the one or more sensory inputs to the first        cluster position comprises generating a feature set or model of        the one or more sensory inputs, and mapping the feature set or        model to the first cluster position.        Clause 9. The method of clause 1 or of any clause herein,        further comprising:    -   receiving, by the mobile device, the one or more sensory inputs;        utilizing, by the mobile device, a machine learning algorithm to        generate, based on the one or more sensory inputs a feature set        representing the one or more sensory inputs; and        hashing, by the mobile device, the feature set to obtain an        expected cluster that is specific to an individual associated        with the mobile device.        Clause 10. The method of clause 9 or of any clause herein,        wherein the machine learning algorithm is specifically        configured to an individual associated with the mobile device.        Clause 11. The method of clause 1 further comprising:    -   authorizing, by the POS device, the transaction when the first        cluster position of the at least one particular cluster does        correspond to the second cluster position of the at least one        expected cluster.        Clause 12. The method of clause 1 or of any clause herein,        wherein the at least one second factor authentication process        comprises generating an alert to a merchant associated with the        POS device.        Clause 13. The method of clause 1 or of any clause herein,        wherein the at least one second factor authentication process        comprises generating an alert to a financial services entity        associated with one or more of the POS device, the card, and/or        the owner of the mobile device.        Clause 14. A point of service (POS) device comprising:    -   at least one card reading component configured to read        information from a transaction card, the at least one card        reading component comprising one or more of a magnetic stripe        reader, a chip reader, and/or a first near field communication        (NFC) component;    -   at least one mobile device transceiver component configured to        communicate, during execution of a purchase transaction, with a        mobile device presented for payment, the mobile device        transceiver component comprising a second NFC component; and    -   one or more processing components and/or computer readable media        configured for: obtaining, from one or both of the transaction        card and/or the mobile device, upon initiation of the        transaction:        -   (i) one or more sensory inputs associated with a use of the            one or both of the card and the mobile device for the            transaction; and        -   (ii) an identifier (1) associated with a known owner of the            one or both of the card and the mobile device and (2) used            to determine whether the one or more sensory inputs are            consistent with prior transaction behavior of the owner;    -   mapping the one or more sensory inputs to a first cluster        position of at least one particular cluster of a plurality of        clusters,        -   wherein the plurality of clusters defines sets of hashed,            learned features regarding prior known interactions of            owners of cards with POS devices, the prior known            interactions being mapped by machine learning into the            plurality of clusters such that prior sensory input of each            owner has been mapped to at least one expected cluster;    -   determining when the cluster position of the at least one        particular cluster mapped for the transaction matches a second        cluster position of the at least one expected cluster associated        with the known owner of the one or both of the card and mobile        device; and    -   initiating at least one second factor authentication process to        establish that the individual transacting with the POS device is        the known owner of the one or both of the card and the mobile        device being used in the transaction when the first cluster        position of the at least one particular cluster does not        correspond to the second cluster position of the at least one        expected cluster.        Clause 15. The device of clause 14 or of any clause herein,        wherein the plurality of clusters are provided to the POS device        via provision of a smart card or computer-readable medium from        an entity associated with the one or both of the card and the        mobile device and/or an entity involved with preventing        fraudulent transactions.        Clause 16. The device of clause 14 or of any clause herein,        wherein the plurality of clusters are provided to the POS device        via download from one or more servers associated with at least        one entity involved with preventing fraudulent transactions.        Clause 17. The device of clause 14 or of any clause herein,        wherein the second factor authentication is performed at the POS        device.        Clause 18. The device of clause 14 or of any clause herein,        wherein the determining when the cluster position of the at        least one particular cluster matches the second cluster position        is performed at the POS device, such that a fraud determination        may be performed on-site at the POS device without need for        communicating with remote entities to make the fraud        determination.        Clause 19. The device of clause 14 or of any clause herein,        wherein the one or more sensory inputs comprise one or more of:    -   (i) features of the card including one or more of dimension(s)        of card, name on card, and/or material and/or color of card;    -   (ii) location and/or movement of the card and/or the mobile        device; and/or    -   (iii) biometric or other information regarding the individual        transacting with the card and/or mobile device.        Clause 20. The device of clause 14 or of any clause herein,        wherein mapping the one or more sensory inputs to the first        cluster position comprises direct mapping of the one or more        sensory inputs to the first cluster position.        Clause 21. The device of clause 14 or of any clause herein,        wherein mapping the one or more sensory inputs to the first        cluster position comprises generating a feature set or model of        the one or more sensory inputs, and mapping the feature set or        model to the first cluster position.        Clause 22. The device of clause 14 or of any clause herein,        wherein the one or more processing components and/or computer        readable media are further configured for:    -   receiving, by the mobile device, the one or more sensory inputs;        utilizing, by the mobile device, a machine learning algorithm to        generate, based on the one or more sensory inputs a feature set        representing the one or more sensory inputs; and        hashing, by the mobile device, the feature set to obtain an        expected cluster that is specific to an individual associated        with the mobile device.        Clause 23. The device of clause 22 or of any clause herein,        wherein the machine learning algorithm is specifically        configured to an individual associated with the mobile device.        Clause 24. The device of clause 14 or of any clause herein,        wherein the one or more processing components and/or computer        readable media are further configured for:    -   authorizing, by the POS device, the transaction when the first        cluster position of the at least one particular cluster does        correspond to the second cluster position of the at least one        expected cluster.        Clause 25. The device of clause 14 or of any clause herein,        wherein the at least one second factor authentication process        comprises generating an alert to a merchant associated with the        POS device.        Clause 26. The device of clause 14 or of any clause herein,        wherein the at least one second factor authentication process        comprises generating an alert to a financial services entity        associated with one or more of the POS device, the card, and/or        the owner of the mobile device.        Clause 27. Embodiments herein may also take the form of a system        comprised of computing elements that are arranged, programmed        and/or otherwise adapted to perform the features and        functionality set forth anywhere above. Such computing elements        may include and/or involve computer readable media.        Clause 20. In addition, embodiments herein may also take the        form of one or more computer readable media containing        computer-executable instructions for performing any of the        processing herein, the computer-executable instructions being        executable via one or more processing components to process        instructions and/or perform one or more aspects of the        functionality set forth herein.

While one or more embodiments of the present disclosure have beendescribed, it is understood that these embodiments are illustrativeonly, and not restrictive, and that many modifications may becomeapparent to those of ordinary skill in the art, including that variousembodiments of the inventive methodologies, the inventivesystems/platforms, and the inventive devices described herein can beutilized in any combination with each other. Further still, the varioussteps may be carried out in any desired order (and any desired steps maybe added and/or any desired steps may be eliminated).

1. A method comprising: obtaining, by a point-of-service (POS) device,upon initiation of a transaction involving (1) a card or (2) the cardand a mobile device associated with an individual transacting with thePOS device: (i) one or more sensory inputs associated with a use of theone or both of the card and the mobile device for the transaction, theone or more sensory inputs comprising one or more card-acquired sensoryinputs received from one or more sensors on the card; wherein the one ormore sensory inputs comprising sensory feedback measured by the one ormore sensors based on usage of the card for the transaction; and whereinthe one or more sensory inputs are saved into a secure format by thecard, via encryption or a hash, as a feature set, and transmitted to thePOS device upon the initiation of the transaction; and (ii) anidentifier (1) associated with a known owner of the one or both of thecard and the mobile device and (2) used to determine whether the one ormore sensory inputs are consistent with prior transaction behavior ofthe owner; unlocking, by the POS device, the feature set to obtain theone or more sensory inputs for processing at the POS device;transforming, using a machine learning technique by the POS device, theone or more sensory inputs associated with the transaction into featureinformation having a format configured for comparison against historicalfeature data established by applying the machine learning technique tohistorical card-usage information of the owner; performing sensory inputmapping, by the POS device, of the feature information to a clusterposition of at least one particular cluster of a plurality of clustersstored within memory of the POS device, wherein the plurality ofclusters defines sets of hashed, learned features regarding prior knowninteractions of owners of cards with POS devices, the prior knowninteractions being mapped by the machine learning technique into theplurality of clusters such that prior sensory input of each owner hasbeen mapped to at least one expected cluster; performing, by the POSdevice, a fraud determination based on mapping of the cluster positionof the at least one particular cluster to the at least one expectedcluster associated with the known owner of the one or both of the cardand mobile device; and approving the transaction based on the frauddetermination.
 2. The method of claim 1 wherein the plurality ofclusters are provided to the POS device via a smart card (SIM card) orother computer-readable medium that is provided from an entityassociated with the one or both of the card and the mobile device and/oran entity involved with preventing fraudulent transactions.
 3. Themethod of claim 1 wherein the plurality of clusters are provided to thePOS device via download from one or more servers associated with atleast one entity involved in the prevention of fraudulent transactions.4. The method of claim 1 wherein the second factor authentication istriggered via processing performed by the POS device, and the secondfactor authentication is performed at the POS device.
 5. The method ofclaim 1 wherein the determining when the cluster position of the atleast one particular cluster mapped for the transaction matches thesecond cluster position is performed at the POS device, such that thefraud determination may be performed on-site at the POS device withoutneed for communicating with remote entities to make the frauddetermination.
 6. The method of claim 1 wherein the one or more inputscomprise one or more of: (i) features of the card including one or moreof dimension(s) of card, name on card, and/or material and/or color ofcard; (ii) location and/or movement of the card and/or the mobiledevice; and/or (iii) biometric or other information regarding theindividual transacting with the card and/or mobile device.
 7. The methodof claim 1 wherein mapping the one or more sensory inputs to the firstcluster position comprises direct mapping of the one or more sensoryinputs to the first cluster position.
 8. The method of claim 1 whereinmapping the one or more sensory inputs to the first cluster positioncomprises generating a feature set or model of the one or more sensoryinputs, and mapping the feature set or model to the first clusterposition.
 9. The method of claim 1 further comprising: receiving, by themobile device, the one or more sensory inputs; utilizing, by the mobiledevice, a machine learning algorithm to generate, based on the one ormore sensory inputs a feature set representing the one or more sensoryinputs; and hashing, by the mobile device, the feature set to obtain anexpected cluster that is specific to an individual associated with themobile device.
 10. The method of claim 9 wherein the machine learningalgorithm is specifically configured to an individual associated withthe mobile device.
 11. The method of claim 1 further comprising:authorizing, by the POS device, the transaction when the first clusterposition of the at least one particular cluster does correspond to thesecond cluster position of the at least one expected cluster.
 12. Themethod of claim 1 wherein the at least one second factor authenticationprocess comprises generating an alert to a merchant associated with thePOS device.
 13. The method of claim 1 wherein the at least one secondfactor authentication process comprises generating an alert to afinancial services entity associated with one or more of the POS device,the card, and/or the owner of the mobile device.
 14. A point of service(POS) device comprising: at least one card reading component configuredto read information from a transaction card, the at least one cardreading component comprising one or more of a magnetic stripe reader, achip reader, and/or a first near field communication (NFC) component; atleast one mobile device transceiver component configured to communicate,during execution of a purchase transaction, with a mobile devicepresented for payment, the mobile device transceiver componentcomprising a second NFC component; and one or more processing componentsand/or computer readable media configured for: obtaining, from one orboth of the transaction card and/or the mobile device, upon initiationof the transaction: (i) one or more sensory inputs associated with a useof the one or both of the card and the mobile device for thetransaction, the one or more sensory inputs comprising one or morecard-acquired sensory inputs received from one or more sensors on thecard; wherein the one or more sensory inputs comprising sensory feedbackmeasured by the one or more sensors based on usage of the card for thetransaction; and wherein the one or more sensory inputs are saved into asecure format by the card, via encryption or a hash, as a feature set,and transmitted to the POS device upon the initiation of thetransaction; and (ii) an identifier (1) associated with a known owner ofthe one or both of the card and the mobile device and (2) used todetermine whether the one or more sensory inputs are consistent withprior transaction behavior of the owner; unlocking, by the POS device,the feature set to obtain the one or more sensory inputs for processingat the POS device; transforming, using a machine learning technique, theone or more sensory inputs associated with the transaction into featureinformation having a format configured for comparison against historicalfeature data established by applying the machine learning technique tohistorical card-usage information of the owner; performing sensory inputmapping, by the POS device, of the feature information to a firstcluster position of at least one particular cluster of a plurality ofclusters stored within memory of the POS device, wherein the pluralityof clusters defines sets of learned features regarding prior knowninteractions of owners of cards with POS devices, the prior knowninteractions being mapped by the machine learning technique into theplurality of clusters such that prior sensory input of each owner hasbeen mapped to at least one expected cluster; and wherein the sensoryinput mapping is performed such that a fraud determination is performedat the POS device; performing, by the POS device, a fraud determinationbased on mapping of the cluster position of the at least one particularcluster to the at least one expected cluster associated with the knownowner of the one or both of the card and mobile device; and approvingthe transaction based on the fraud determination.
 15. The device ofclaim 14 wherein the plurality of clusters are provided to the POSdevice via provision of a smart card or computer-readable medium from anentity associated with the one or both of the card and the mobile deviceand/or an entity involved with preventing fraudulent transactions. 16.The device of claim 14 wherein the plurality of clusters are provided tothe POS device via download from one or more servers associated with atleast one entity involved with preventing fraudulent transactions. 17.The device of claim 14 wherein the second factor authentication isperformed at the POS device.
 18. The device of claim 14 wherein thedetermining when the cluster position of the at least one particularcluster matches the second cluster position is performed at the POSdevice, such that a fraud determination may be performed on-site at thePOS device without need for communicating with remote entities to makethe fraud determination.
 19. The device of claim 14 wherein mapping theone or more sensory inputs to the first cluster position comprisesdirect mapping of the one or more sensory inputs to the first clusterposition.
 20. The device of claim 14 wherein mapping the one or moresensory inputs to the first cluster position comprises generating afeature set or model of the one or more sensory inputs, and mapping thefeature set or model to the first cluster position.